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Peer Influence, Internet use andCyberbullying: A Comparison ofDifferent Context Effects amongGerman AdolescentsRuth Festl , Michael Scharkow & Thorsten QuandtPublished online: 21 Mar 2013.

To cite this article: Ruth Festl , Michael Scharkow & Thorsten Quandt (2013): Peer Influence,Internet use and Cyberbullying: A Comparison of Different Context Effects among GermanAdolescents, Journal of Children and Media, DOI:10.1080/17482798.2013.781514

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PEER INFLUENCE, INTERNET USE AND

CYBERBULLYING: A COMPARISON OF

DIFFERENT CONTEXT EFFECTS AMONG

GERMAN ADOLESCENTS

Ruth Festl, Michael Scharkow and Thorsten Quandt

The influence of social reference groups such as family members, classmates and friends on

adolescents’ attitudes and behavior has been acknowledged in research for many decades. With

the increasing use of online media, cyberbullying has become a major issue in adolescence

research. In this paper, we compare various forms of peer influence on cyberbullying behavior

among high school students in Germany. Specifically, the impact of close friends and more distant

peers in the school class on perpetrator and victim roles is compared. The results indicate that the

class context is highly relevant for cyberbullying. For both processes—perpetration and

victimization—the number of cyberbullies within a school class plays an important role in

predicting individual behavior. Looking at individual risk factors, the results show that

cyberbullying is strongly related to the use of social networking sites, and the risk of victimization

increases with the time spent online.

KEYWORDS cyberbullying; peer influence; internet use; adolescence research; social network

analysis; school survey; Germany

Introduction

The notion that people are subject to personal influence by their peers has been

acknowledged in the social sciences for many decades (Deutsch & Gerard, 1955; Katz &

Lazarsfeld, 1955; Kelman, 1958). Especially within adolescence research, there is a large

body of literature on the question of how children and adolescents are influenced by

different social reference groups such as families, friends and school classes (summarized by

Cotterell, 2007). Theoretically, there are many plausible explanations of how social influence

is exerted and accepted between peers. Explanations range from indirect peer group

effects through cognitive and affective processes within the individual—like imitation,

social comparison, competition, group conformity and norms (Berten, 2008)—to direct

peer group effects through contacts and interactions (e.g., Sieving, Perry, & Williams, 2000).

Depending on the attitude or behavior under investigation, it is not only necessary to

consider how, but more basically which persons exert influence. Family members may be

more influential concerning eating habits, whereas academic performance can also be

subject to class or school effects. Accordingly, many empirical studies include contextual

factors on the class or school level when explaining individual behavior (e.g., Mayberry,

Espelage, & Koenig, 2009). Previous research has demonstrated the importance of social

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influence for youth violence and aggression (e.g., Cairns, Cairns, Neckerman, Gest, &

Gariepy, 1988; Ferguson, San Miguel, & Hartley, 2009). This is also true for bullying as an

example of adolescents’ aggressive behavior (Burns, Maycock, Cross, & Brown, 2008;

Ferguson et al., 2009).

As the use of information and communication technologies (ICTs) among

adolescents has increased in the last decade, school bullying is no longer restricted to

face-to-face communication, but also happens online. Prevalence studies show that 20 to

40 per cent of all youth have already experienced cyberbullying (e.g., meta-analysis by

Tokunaga, 2010). Until now, research on cyberbullying has mainly focused on individual risk

factors and psychological explanations as well as media use (e.g., Walrave & Heirman, 2009;

Ybarra & Mitchell, 2008). However, as Ferguson, Winegard, and Winegard (2011) argue,

media effects can also be viewed as social influence, since the individually perceived

content is produced and distributed by others—institutions or (known and unknown)

persons. In this paper, we compare different context effects, most importantly peer

influence and internet use, for cyberbullying behavior among German high school

students. We also assume that different types of reference groups, notably close friends and

classmates, may have different effects on victims and perpetrators and therefore need to be

considered when analyzing cyberbullying.

Theoretical Considerations

The Influence of the Social Environment—Peers and Media Use

Although most social scientists will agree that peers influence individual behavior,

there is considerable disagreement on how these influence mechanisms work—especially

in the context of deviant behavior such as cyberbullying. Compared to media effects

research on television or computer games, the role of the internet for cyberbullying is even

more multi-faceted because it not only provides media content, but also actual

opportunities for perpetrators and risks for victims through online exposure, and finally

enables direct observation of other persons’ online behavior. Following this line of

argument, in cyberbullying research, media use influence must be considered from another

perspective, as every cyberbullying-based behavior happens in a media context: ICT-based

communication constitutes condition and channel at the same time. In the following, we

therefore discuss different approaches of social, i.e. peer and media influence.

One of the most prominent theoretical frameworks on the topic of social influence is

the Social Learning Theory by Bandura (1977). He suggests that the behavior of a person is

affected by direct observation of other people’s behavior. Accordingly, peers can be

regarded as potentially positive or negative role models. Bandura (1978, p. 14) argues that

“people are not born with performed repertoires of aggressive behavior; they must learn

them.” Furthermore, he emphasizes that aggression is prompted by the anticipated

positive consequences of the behavior; in the case of cyberbullying this is often the pursuit

of social goals such as dominance or prestige (Sijtsema, Veenstra, Lindenberg, & Salmivalli,

2009). Therefore, persons do not thoughtlessly mimic or react to the observed model, but

modify their own behavior according to individual motives and expected consequences

(Bandura, 1978). This conceptualization of an active rather than passive role is even more

pronounced in recent work by Ferguson et al. (2008). The authors assume that people who

are genetically predisposed to aggression will be more likely to search for violent modeling

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options—also in the context of media—that are consistent with their predispositions.

Consequently, peer influence and media use as environmental factors—so-called “stylistic

catalysts”—only moderate the causal relationship between individual traits and aggressive

behavior.

Recent research on peer influence distinguishes between active effects via explicit

communication and passive effects through more or less unconscious processes such as

observation or social comparison (e.g., Ferguson et al., 2011). This logic of direct and

indirect influence can also be expanded to include media use: although individuals have

only direct contact to a small part of their social environment, they can also be influenced

by more distant role models presented via different forms of mass media (Bandura, 1978).

Bandura’s theory was developed in the seventies and therefore mainly focused on

television actors as unknown role models when analyzing media influence. These role

models only exert indirect influence through presenting certain kinds of behavior or norms.

Media use influence on cyberbullying can also happenmore directly, especially through the

use of the Internet. Although mediated through ICTs, people can directly motivate a

perpetrator to harass another person by verbally or non-verbally supporting the

perpetrator’s behavior. This media use influence can not only be performed by unknown

individuals, but also by peers from the offline context. Many studies have shown that even

though cyberbullying happens in global online social networks, most of the attacks

are targeted on people also known from real-life (e.g., Jager, Fischer, & Riebel, 2009).

Following the arguments above media influence is also split into direct and indirect aspects

(see Table 1).

Finally, it should be noted that the influential power of peers is not only rooted in

their mere presence through imitation of their behavior, but also comes from more

structural aspects such as the social norms they provide or the social position they occupy.

Social norms are defined as implicit (or sometimes also explicit) rules for appropriate values,

beliefs, attitudes and behavior (e.g., Miller & Prentice, 1996). According to the theory of

planned behavior (Ajzen & Fishbein, 1980; Ajzen, 1991), subjective perceived norms play an

essential part for the explanation of certain kinds of behavior. Together with the person’s

attitude toward the behavior as well as the perceived behavioral control, these determine

the intention to perform the considered behavior.

TABLE 1

Examples of peer influence and internet use on cyberbullying behavior

Peer Influence Media-based (Peer) Influence

Direct Verbal support of theperpetrator’s behavior

Media-based verbal or non-verbalsupport of the perpetrator’s behavior (e.g., throughcommentary options or like-buttons)

Indirect Providing an audience throughobservation of the bullyingin the schoolyard

Providing an audience throughwitnessing the bullying inthe internet (e.g., throughthe public reception ofYouTube-videos)

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The Question of the Reference Group

When analyzing the mechanisms of social influence, there is always one basic

question: who do we compare with, or more generally, who do we refer to? Sherif (1968,

p. 86) generally defines these social entities as “groups to which the individual relates

himself as a part or to which he aspires to relate himself psychologically.” Studies could

show that parents and peers are the most relevant reference groups for adolescents,

although peers become increasingly important in the transitional phase between

childhood and adolescence (e.g., Brown, 1990; Larson & Richards, 1991; Simmons & Blyth,

1987). It has to be noted that the term peers is often used as a very broad “catch-all”

concept, including friends as well as other looser forms of acquaintances. The respective

structures united under the label peer group can range from very exclusive cliques to peer

crowds and other loose groups (Berten, 2008; Cotterell, 2007; Jessor & Jessor, 1977).

According to reference theory (Merton & Rossi, 1968), we do not only refer to people

we are directly connected to and, consequently, are not only influenced by close friends

(Payne & Cornwell, 2007). Marsden and Friedkin (1993) argue that the only precondition of

social influence is some form of information regarding others’ attitudes or behaviors—

providing the ego with the possibility to compare oneself with these individuals. Therefore,

social influence is not restricted to face-to-face interaction, but can also be accomplished by

a non-visible or even imagined opposite—a fact that constitutes the basis for many

communication theories such as the Spiral of Silence (Noelle-Neumann, 1984). However,

most of the studies dealing with social influence among adolescents do not focus on non-

observable partners, but rather have contrasted directly connected cliques with less

proximate social crowds (e.g., Payne & Cornwell, 2007). Cotterell (2007, p. 60) defines the

peer crowd as a “type of social network that contains several cliques, loosely linked

together.” Brown, Eicher, and Petrie (1986) maintain that this term is not so much based on

social interaction between adolescents, but more strongly relates to their reputation.

Regarding the influence of these peer types, several studies found that both cliques and

peer crowds were influential for adolescents’ substance use (Hussong, 2002) or their risk-

taking behavior (Payne & Cornwell, 2007), with best friends appearing to be more powerful

in these social processes.

Cyberbullying and Peer Influence

Studies on peer influence in adolescence have mostly focused on substance use and

health-related behaviors. For example, studies could confirm a positive association

between self- and peer-related risk behavior concerning smoking habits and drug use (e.g.,

Kirke, 2004; Maxwell, 2002; Sieving et al., 2000), sexual activities (e.g., Jaccard, Blanton, &

Dodge, 2005; Sieving, Eisenberg, Pettingell, & Skay, 2006) as well as physical exercise and

health (e.g., de la Haye, Robins, Mohr, & Wilson, 2010; Macdonald-Wallis, Jago, Page,

Brockman, & Thompson, 2011). Bullying is similar to these issues in the sense that there are

both indirect and direct peer effects: an adolescent can talk about or witness bullying

between classmates or be directly victimized. However, we argue that there is an important

difference between bullying and substance-use or sexual activities: the relevance of school

classes as reference groups. The latter are mostly private activities shared with close friends.

Bullying and especially cyberbullying, in contrast, often happen in a more public context

and supposedly involve not only close, but also more distant peers. Moreover, the class

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context can have a self-contained influence on cyberbullying. For instance, the risk of

victimization may be related to bullying-related norms (also depending on the teacher’s

behavior), or simply according to the number of bullies in class.

For the present study we followed the definition of Smith, Mahdavi, Carvalho, Fisher,

and Russell (2008, p. 376) who describe cyberbullying as an “aggressive, intentional act

carried out by a group or individual, using electronic forms of contact, repeatedly and over

time against a victim who cannot easily defend him or herself.” The definition contains all

relevant aspects of traditional bullying, supplemented by the performance of the behavior

via electronic communication technologies. This implies a similarity between traditional

bullying and cyberbullying—an assumption shared by many scholars dealing with this

subject of research (e.g., Kowalski & Limber, 2007; Raskauskas & Stoltz, 2007). Therefore,

it can also be assumed that some peer influence mechanisms are similar for bullying and

cyberbullying.

Recent research on bullying has not only focused on individual risk factors, but also

on the socio-structural attributes of perpetrators and victims. Salmivalli, Lagerspetz,

Bjorkqvist, Osterman, and Kaukiainen (1996) found that peers rated victims of traditional

bullying high on social rejection and, inversely, low on social acceptance. This was also true

for male perpetrators, whereas ratings for female bullies were inconsistent. Sijtsema et al.

(2009) could confirm the low social preference for female victims and younger female

perpetrators. However, the latter were also perceived as popular. Causal directions are not

clear in the studies, as the findings are based on cross-sectional data. Nonetheless, it can be

assumed that popularity influences the risk of being involved in (cyber-) bullying, especially

for the victims. Unpopular students are expected to have less protection from sympathetic

peers and are easy targets for perpetrators who want to improve their own social standing

(see Sijtsema et al., 2009).

Ferguson et al. (2009) conducted a multivariate analysis of youth violence and

aggression and tested the influence of different personality-, peer- and media-related

aspects on traditional bullying behavior. Among a range of other factors, such as negative

relations with adults and exposure to video game violence, the authors identified

antisocial personality traits and self-reported delinquent peers as being the strongest

predictors for individual perpetration. Mouttapa, Valente, Gallaher, Rohrbach, and Unger

(2004) found that bullies and also so-called aggressive victims (students who have already

experienced both bullying and victimization) nominate aggressive friends more often.

The mere existence of aggressive peers seems to increase the risk of actively participating

in aggression—a result that could be confirmed by Rulison, Gest, Loken, and Welsh

(2010). Salmivalli, Huttunen, and Lagerspetz (1997) did not focus on general peer

attitudes such as aggression and delinquency, but directly analyzed the bullying behavior

of peers. They identified students with different behavioral roles within the bullying

process, as well as specific forms of peer configurations. They showed that adolescents

often affiliate with peers exhibiting the same, or a complementary, bullying behavior.

Victims, for example, are often friends with other victims or so-called defenders, who

actively stand up for victimized persons. Accordingly, bullies are more often friends with

other bullies or assistants and reinforcers, who more or less actively support their

behavior (Salmivalli et al., 1997).

These results emphasize the existence of peer influence effects within the field of

traditional bullying behavior. It has to be noted, though, that most of the studies focus on

peer groups as close, cohesive friendship networks, whereas other looser forms of social

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environment are mostly neglected. However, we assume that more abstract units of

influence, like the school class, provide a social setting that has to be considered when

analyzing peer effects. Although not directly tested in the current study, we follow Withall’s

understanding of the social-emotional climate in school classes. According to Gazelle

(2006), this concept “refers to global classroom atmosphere and the degree to which the

classroom as a whole functions smoothly and harmoniously and is characterized by

interactions with a positive tone or, conversely, by frequent disruption, conflict, and

disorganization. Thus, positive as well as negative interactions (e.g., excitement and humor

vs. conflict) contribute to climate” (p. 1180). Not only the teacher’s behavior, but also his or

her interactions with the students, as well as the students’ interactions among themselves,

contribute to this understanding of class climate. Although Gazelle (2006, p. 1180)

emphasizes that “classroom emotional climate is more than the sum of evaluations of single

actors,” we expect that an accumulation of individuals prone to aggressive behavior will

disrupt the overall class climate. Specifically for cyberbullying, a German study confirmed

that perpetrators and their victims are often classmates (Jager et al., 2009). Classes with a

high amount of past bullying not only directly increase the risk for future bullying, but a

climate of aggression can indirectly increase individuals’ participation in aggression

(Mouttapa et al., 2004; Rulison et al., 2010).

Cyberbullying and Internet Use

The very definition of cyberbullying suggests that the phenomenon must also be

considered in the context of media-based environmental influence factors. In the present

study, we therefore focus on several aspects of media use and compare these effects to

different aspects of peer influence. Media use can be viewed from different perspectives

and, in the case of cyberbullying, primarily deals with user behavior on the Internet.

On the one hand, previous studies concentrated on the mere frequency of internet use

and some forms of its applications. Walrave and Heirman (2009) found that, compared

to non-involved persons, perpetrators of cyberbullying spent more time online. In

contrast, the findings of Smith et al. (2008) showed no increased internet use for

perpetrators, whereas victims were reported as using the Internet more intensively.

These differing results may be due to the fact that content-related aspects and the

variety of internet use are not taken into account. Different forms of online behavior, for

example the frequent use of social media applications, are supposed to lead to different

involvement in and opportunities for virtual attacks. Livingstone, Haddon, Gorzig, and

Olafsson (2011) generally revealed that a larger individual repertoire of online activities is

related to risky online behavior such as cyberbullying. More specifically, Ybarra and

Mitchell (2008) differentiated between different forms of online activities. They could

show that compared to non-involved students, cybervictims more often used social

media such as instant messaging, chat rooms or social networking sites. Walrave and

Heirman (2009) confirmed that not only victims, but also perpetrators, more often

participate in open chats. Following these findings, it must be concluded that frequent

use of the Internet correlates with an enhanced risk of becoming involved in

cyberbullying for both perpetrators and victims. Internet use provides possibilities for

bullying or risks for being attacked. In particular, intensive use of social media coincides

with cyberbullying involvement.

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Research Questions

For the present study, the school setting was chosen as the relevant context. In a

school environment, various groups can be potentially influential. The most persistent

group, at least in the German case, is the class: students spend many years of their most

important socialization period in that (relatively) stable structure. While group membership

is not something that students can decide upon themselves, they still form further informal

connections like smaller friendship networks inside the class. In that sense, the school class

as a social entity also corresponds to Cotterell’s (2007) definition of a peer crowd. Early

studies already point to the relevance of the school class in shaping peer relations, mostly

focusing on social homophily with respect to race and gender (e.g., Shrum, Cheek, &

Hunter, 1988).

However, the main interest of the present study is not only to pick friends out of the

class, but also to compare these to the whole class as a self-contained level of influence

itself. This consideration reflects the assumption that class-specific aspects like the general

climate or the teacher’s influence create a particular atmosphere, which also must be

considered as relevant factors in forming young people’s behavior. This can be understood

as an independent meso-level effect, beyond the simple sum of individual friendship

effects. Other factors of social influence, like the school atmosphere, general social

environment/milieu etc. might also be considered. However, when it comes to the more

plausible factors in the given context of cyberbullying, the direct friendship effects, as well

as the class level seem to be the more relevant ones, as the phenomenon itself is usually

directly linked to these groups. Last but not least, we need to consider media-related

influences. In the given case, the most relevant factor is, naturally, ICT use, as this enables

cyberbullying in the first place. The amount of ICT use is regarded as being a catalyst for

cyberbullying in the literature (Walrave & Heirman, 2009). Furthermore, particular

applications of ICT use, specifically social-based activities, are also supposed to influence

the risk of virtual perpetration and victimization (e.g., Ybarra & Mitchell, 2008).

Individual variables

Age, gender,Popularity

Media exposure

Frequency of internetuse; intensity of social

network use

Peer influence

Class cyberbullying,friends cyberbullying

Cyberbullying risk

Perpetration andvictimization

FIGURE 1

Research model of peer influence and internet use on cyberbullying risk

PEER INFLUENCE, INTERNET USE AND CYBERBULLYING 7

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Following these considerations, we developed a research model (see Figure 1) and

attempted to answer the following questions:

(1) Are friendship networks a relevant factor of influence in cyberbullying behavior, and if so,

in what way do they influence that behavior?

(2) Is the school class a relevant, independent level of influence on cyberbullying behavior,

and if so, in what way does it influence the behavior?

(3) Is there a combined friendship/class influence, explaining more than the independent

effects (1.) and (2.)?

(4) Is ICT use a relevant factor of influence on cyberbullying behavior, and if so, in what way

does it influence the behavior?

Naturally, these are very broad research questions. However, the aim of the present

study is to show an application of peer influence models in adolescence research rather

than to provide a complete explanation of cyberbullying behavior.

Method

Participants

As noted above, we tested the levels of influence in a first pilot study. In a German

high school (academic track secondary school, Gymnasium), we conducted a full school

survey, including questions on cyberbullying, ICT use, friendship networks and various

other levels of influence. The sample consisted of 276 high school students. Due to ethical

and legal issues, we only collected data from seventh-grade students and above. Their ages

ranged between 13 and 19 years with an average age of 15.5. Male students were slightly

overrepresented, accounting for 57 per cent of the sample. On average, the adolescents

spent 2.3 h per day on the Internet. All of the participants had access to at least a shared

computer in the household, and 75.8 per cent had their own PC.

The sample was drawn from 14 classes with an average size of 21.1 students per class.

Class size ranged from 8 to 67 students, whereby it must be mentioned that, according to

the German school system, senior students are not distributed into classes, but rather,

organized in a broader group (“Stufe,” here: N ¼ 67). Classes strongly varied in their average

daily internet use, ranging from 54min to 5.5 h per day. Moreover, we observed a large

variance in the classes’ connectedness. The mean value for out-degree (number of friends

named) per class was between 0.7 and 6.7, the mean value for in-degree (number of friend

nominations received) ranged between 0.8 and 5.0. Hence, some classes could be

characterized by a stronger connection between their members, whereas others seemed to

be only loosely connected—this variation already supports the argument that classes

derive from a specific environment in which differing social networks develop.

Measures and Analytical Procedures

Following our research questions, we considered (a) cyberbullying behavior, (b)

friendship networks, (c) class-level bullying and victimization, and (d) other control factors,

including internet use.

Cyberbullying behavior. In order to measure comparable mental concepts of

cyberbullying, a short explanation of the behavior on the basis of Smith et al. (2008) was

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presented in the questionnaire. Following the short explanation, we asked the participants

to indicate their cyberbullying experience within the last year, both as perpetrator and

victim. Both roles were measured using an independent dichotomous variable,

so combinations of the roles (perpetrator/victim) were possible.

Friendship networks. In addition to the standard questionnaire, respondents were

asked to nominate their friends in school, whether in the same class or not. The

nominations were subsequently transferred into pseudonymous data, still retaining the

underlying network structure. Therefore, we were able to calculate standard network

indices. On average, a respondent had 3.1 (reciprocal) friendship nominations with out-

degree values ranging from 0 to 11 (SD ¼ 1.97) and in-degree values ranging from 0 to

13 (SD ¼ 2.24). In- and out-degree showed a strong positive correlation (r ¼ .47, p , .001).

In-degree can be considered as an indicator of popularity among schoolmates.

We specified a direct influence (contagion) model based on cohesion (i.e., friendship).

This means that the influence of friends is modeled using an asymmetric adjacency matrix

with the clique-based precondition of friendship nominations. In other words, the peer

influence term in the regression is the average of those people named as friends. Note that

for simplicity, we do not consider higher-order connections, such as friends-of-friends, in

our adjacency matrix (while such an influence might be plausible, it is equally plausible that

it is much weaker than the direct effects).

Through the procedure of row normalization we then created the so-called weight

matrix W: The matrix is thereby weighted proportional to a person’s out-degree and deals

with the influence exerted on this person (ego) (Leenders, 2002). As depicted in Figure 2,

the procedure weights the importance of the particular peer according to their overall

number in ego’s friendship network in school.

In the given example, Peter nominates three friends (C, E, F), whereas Julia is only

connected to person D. Within the row-normalized weight matrix, all values in a row must

sum to 1, so that the influence exerted on a person (for example Peter) is divided by the

number of friends nominated (in this case 3). Julia, on the other hand, is only exposed to the

influence of D, whose score is fully retained and therefore weighted by 1. In a subsequent

step, friendship influence is calculated based on this weight matrix. The cybermobbing

behavior of the alteri (e.g., being a bully ¼ 1 and not being a bully ¼ 0) is multiplied with

their weighted size of influence on ego. If persons C and D are cyberbullies, the friendship

A

0Peter

0Julia

Michael 1

B

0

0

1

C

1

0

0

D

0

1

1

E

1

0

1

F

1

0

0

A

0Peter

0Julia

Michael .25 .25

B

0

0

C

.33

0

0

D

0

1

.25

E

.33

0

.25

F

.33

0

0

FIGURE 2

Example for an adjacency matrix (above) and a row normalized weight matrix (below)

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influence factor for Peter is calculated as .33 * 1 þ .33 * 0 þ .33 * 0 ¼ .33. Since the

cyberbullying variable is dichotomous, the score is the percentage of cyberbullies among

the respondent’s friends.

Class level bullying and victimization. In contrast to the cohesive peer group

measure, we also modeled social influence on the class level, following our research

questions. For each class, we therefore calculated a mean value of bullying and

victimization cases. This specification is often called the simple context effect model

(Erbring & Young, 1979). It is noteworthy that the simple class mean can be seen as a special

case of peer influence models. Influence is exerted through co-membership in a fixed social

group, and the weight matrix is constructed using a special symmetric sociomatrix in which

ties exist between all students of the same class and no ties exist between classes. As before,

the peer score is then computed as the unweighted mean of all classmates’ scores and the

respondent him or herself.

ICT use. We included two variables on ICT use to measure individual media

exposure. First, we asked the participants about the frequency of their daily internet use,

indicated in hours per day. Moreover, we collected data about the number of different

social network sites that are actively visited by the students. The latter is used as an

indicator for the amount of socially-based internet activities of an individual.

Data analysis was carried out using the statistical software R and the sna package

(Butts, 2008). The hypothesized models were tested using multiple logistic regression

analyses because cyberbullying roles were measured as dichotomous variables.

We excluded missing data list wise and followed the common significance levels:

p , .05 ¼ *, p , .01 ¼ **, p , .001***.

Results

Descriptive Findings

The prevalence rates of cyberbullying on individual level as well as on class level were

in line with other studies in that field (e.g., Tokunaga, 2010). We observed a slightly higher

percentage of students being a cyberbully (13 per cent) compared to the number of

cybervictims (11 per cent). On the aggregate level, we found at least one cyberbully or

victim per class, with a mean value of over four cases per class. For both indices—average

number of bullies and average number of victims in class—values varied widely, ranging

from 0 to .26 (bullies), respectively from 0 to .24 (victims).

Levels of Influence on Being a Perpetrator

In the first step, we focused on the risk of becoming a cyberbully (see Table 2). To

compare the predictive power of different peer influence models and media exposure, we

calculated alternative logistic regression models. For easier interpretation of the results, the

class mean was rescaled into 10 per cent intervals before estimating the class model.

Cyberbullying in class was identified as a strong predictor of individual perpetration. If the

percentage of bullies in a class rises by 10 per cent, the individual has an almost three-time

enhanced risk of also becoming a cyberbully (EXP(B) ¼ 2.94, p , .01). Inmodel 2, we tested for

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the possibility of direct peer effects, i.e., friends influencing the adolescents’ individual behavior.

The results show that close friends do not significantly affect a person’s cyberbullying

involvement. When focusing on the combined model of friendship and class influence (model

3), the class effect remains and even marginally increases (EXP (B) ¼ 3.04, p , .01). So, the

perpetration role seems to be strongly determined by the general social atmosphere rather

than direct friends. The final model (3) also shows a highly significant media-use influence: An

intensive use of social network sites enhances the risk of becoming a cyberbully, even if the

different levels of peer influence are controlled. In contrast, the mere frequency of internet use

shows no relevant effect. According to these findings, the use of social channels in particular,

seems to provide opportunities for online harassment. Interestingly, neither age and gender of

the participants nor their social acceptance by their mates, played a relevant role in our

sample—they did not reach significance in any of the models.

Levels of Influence on Being a Victim

In contrast to perpetration, victimization in general must be regarded as passive

behavior which is less likely to be affected by forms of contagion. Therefore, we assumed

that the number of victims in class does not matter for individual victimization, whereas the

corresponding bully-percentage must be considered as being relevant. If processes of

contagion exist, they are more likely to appear on the friendship level, since affiliating with

victimized students can enhance personal victimization risk. Finally, we also tested the

hypothesis that a bully within the own friendship network also increases the danger of

becoming a victim, as within such cliques, a more aggressive climate can be expected and

perpetration is supposed to be more common.

As illustrated in Table 3, the results show that the number of bullies within a class

significantly predicts a higher risk of becoming a cybervictim (Model 1: EXP(B) ¼ 1.86,

p , .05; Combined model: EXP(B) ¼ 2.06, p , .05), even after controlling for both bullying

and victimization within the personal friendship network (each with no significant effect).

If the percentage of bullies in class rises by 10 per cent, the individual has a two-time

enhanced risk of being harassed via the Internet. In addition to this peer influence on a class

level, victimization is particularly determined by gender, as female respondents had more

than a four-time enhanced risk of being cyberbullied (EXP(B) ¼ 4.51, p , .01). Similar to

cyber perpetration, a positive influence is also exerted by media exposure. However, more

TABLE 2

Comparing models of peer influence and internet use on the risk of becoming a cyberbully

Class Influence Friend Influence Combined ModelExp(B) Exp(B) Exp(B)

Age 1.13 1.08 1.13Gender (female) .67 .58 .68Daily internet use 1.12 1.10 1.12Social network use 1.63*** 1.65*** 1.64***Popularity .90 .87 .90Class cyberbullying 2.94** – 3.04**Friends cyberbullying – 1.08 0.97

Note. Multiple logistic regression; N ¼ 276; Effect sizes are odd-ratios. Class and friend cyberbullying arerescaled to 10 per cent units.

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decisive for victimization is the time spent online rather than content-related activities.

Nevertheless, it must be mentioned that the effect for the intensity of social network use

(EXP(B) ¼ 1.31), although not significant, is comparable to frequency of internet

use (EXP(B) ¼ 1.20, p , .05). Finally, younger age and lower levels of social popularity

increase the risk of victimization. In particular, the latter is in line with previous research and

stresses the socially problematic aspects of cyberbullying processes.

Discussion

The aim of the present study was to compare the effects of peer influence and media

use in the context of adolescents’ cyberbullying behavior. Our results show that the

classroom context is highly relevant for cyberbullying. For both processes—perpetration

and victimization—the number of bullies within a school class plays an important role in

predicting individual behavior. An aggressive class climate not only seems to motivate

students’ cyberbullying behavior, but also increases the risk of victimization. This result

reinforces the thesis that the school class must be considered as a relevant unit of the social

framework influencing an individual’s behavior. Although students within a class are not

inevitably linked through mutual friendship, they are all connected through daily

co-presence. Different types of peers—friends and (other) classmates—seem to be

important in explaining adolescents’ attitudes and behavior, although their relevance

seems to vary according to characteristics of the considered behavior. As mentioned above,

risk-taking behavior is often the subject of influence by close peers (Payne & Cornwell,

2007). In the present study, we could not confirm this for the case of cyberbullying. In

contrast to other forms of risk behavior, such as substance use, cyberbullying seems to

depend on class context rather than on the direct influence of friends.

In addition to the findings on peer influence, the results also provide evidence that

individual internet use matters for cyberbullying. Perpetration is particularly affected by

intensive use of social networking sites, and the risk of victimization increases with the time

spent online. These results may reflect the degree of activity within the cyberbullying

process. Perpetrators seem to actively search for possibilities of harassment in suitable

online environments, where victims can be easily bullied in front of a (potentially very) large

audience. Social networking sites provide the infrastructure to directly (e.g., verbally insult)

TABLE 3

Comparing models of peer influence and internet use on the risk of becoming a cybervictim

Class Influence Friend Influence Combined ModelExp(B) Exp(B) Exp(B)

Age .80 .80 .80*Gender (female) 3.95** 3.33** 4.51**Daily internet use 1.20* 1.18* 1.20*Social network use 1.29 1.30 1.31Popularity .68* .69* .68*Class cyberbullying 1.86* – 2.06*Friends cyberbullying – .99 .92Friends victimization – .94 .90

Note. Multiple logistic regression; N ¼ 276; Effect sizes are odd-ratios. Class and friend cyberbullying arerescaled to 10 per cent units.

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and indirectly (e.g., spread rumors) offend a person via the Internet. Additionally, the

extensive use of different social networking sites enhances the possibility to bully

anonymously, the perpetrator can switch between different communities and does not

have to worry about being isolated as a consequence of bullying others. In contrast, the

victim’s risk of being attacked rises with the time they linger in cyberspace. This effect is

rather marginal. However, it shows that victims spending lots of time online are likely to be

attacked there, possibly in addition to being attacked in the school environment. Regarding

social networking sites, intensive use is not a strong risk factor. It does not seem to be

important what victims do online, but rather that they can be reached at all. This

accessibility increases with the time spent online. In general, the findings are in line with

previous research indicating that ICT use is a crucial factor to explain cyberbullying

involvement (e.g., Livingstone et al., 2011; Walrave & Heirman, 2009; Ybarra & Mitchell,

2008). Following Ferguson et al. (2011) as well as Gill (2012), media effects are expected to

be smaller or inconsistent compared with peer influence. Although class influence in both

contexts—cyberperpetration and victimization—has an obviously greater effect, we also

found patterns of internet use to be important. This may be due to the fact that the use of

ICTs is an indispensable precondition of exerting and receiving cyberbullying activities.

Compared to traditional bullying or other forms of adolescents’ deviant behavior, media

use is a central part of the phenomenon per se.

Our study shows specifically that class effects and also ICT use seem to be more

pronounced for cyberbullying risk than the influence of close friends. These findings can be

useful for further research on cyberbullying as well as for traditional forms of bullying and

social aggression. Moreover, they may be used to develop recommendations for

prevention and intervention strategies that should focus on both—individual behavior,

particularly the ICT use of students, as well as on aspects of school class structure and

norms.

Limitations and Caveats

As noted above, the purpose of our example study was to compare various context

models on cyberbullying behavior. We acknowledge a number of limitations, some of

which we deliberately accepted for clarity of presentation while others are inherent to the

study design. The limitations concern both the matter of cyberbullying as well as the

specification of the influence models.

We treated cyberbullying-involvement by using two dichotomized groups: bullies

and victims. The current analysis did not account for a possible overlap between these two

groups to keep the overall design manageable. However, it is plausible that different peer

influence mechanisms exist for the group of students who are both perpetrator and victim,

the so-called aggressive victims, who are often supposed to react by retaliation. A class with

a high number of these bully/victims is thought to be characterized by an even higher level

of aggressiveness, contrasted to classes containing solely victims, who are more resigned to

their fate. Besides this group, other roles within the complex social-structural process of

cyberbullying can be expected, such as supporters or bystanders. Salmivalli et al. (1996)

defined different roles relevant within the context of traditional bullying, which more or less

seem to be also relevant for cyberbullying and therefore must be integrated within future

analyses.

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Regarding methodological aspects, our sample only covered one school with 276

students and 14 classes. The school, moreover, represents well-educated students and

cannot claim to be representative, neither for all students in Germany nor for students

elsewhere. Moreover, we only controlled for some basic socio-demographics and ICT-

related variables, neglecting other relevant aspects already confirmed in previous studies.

Since the purpose of our study was to illustrate the consequences of different peer

influence concepts, we intentionally tried to reduce the complexity of the models and did

not try to maximize predictive power by introducing additional variables. In general, our

study is only cross-sectional, so that we cannot distinguish between peer influence and

selection effects. Ideally, peer influence and all other causal models should be applied to

longitudinal data. However, modeling changes in attitude and behavior simultaneously

with changes in social (peer) structure has proven to be quite challenging (Mercken,

Snijders, Steglich, Vartiainen, & de Vries, 2010). Finally, from the large variety of different

model specifications, as detailed by Marsden and Friedkin (1993) as well as Leenders (2002),

we only selected the two most typical designs and did not consider the network-

disturbances model or different weight matrices. It is possible that alternative specifications

could yield plausible results that fit the empirical data.

Despite the limitations in our pilot study, we are confident that using sociometric

data in addition to conventional group variables—such as classes, schools or families—

provides valuable insights into attitude formation and behavior, and the underlying social

processes, especially among adolescents. The use of the concepts presented here is not

limited to problematic behavior but can also help us to understand media use and its

effects in specific social contexts. If peer influence is ubiquitous in childhood and

adolescence, as Cotterell (2007) argues, then the concepts and models introduced in this

paper promise to sharpen our theoretical thinking and enhance empirical research.

ACKNOWLEDGEMENTS

This study was supported by a grant from the German Research Foundation (QU 260/9-1).

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Ruth Festl (author to whom correspondence should be addressed) is research associate at

the University of Hohenheim and the University of Munster, Germany. She received

her Master degree in Education from the LMU Munich. Her research interests include

children, adolescents and media, cyberbullying and digital games. Institute of

Communication Studies, University of Hohenheim, Stuttgart, Germany. E-mail:

ruth.festl@uni-hohenheim.de

Michael Scharkow is research associate at the University of Hohenheim, Germany. He

received his PhD in Communication from the University of the Arts Berlin. His

research interests include empirical research methods, online communication and

media use.

Thorsten Quandt is professor of Communication at the University of Munster, Germany. He

received his PhD in Communication from the TU Ilmenau. His research and teaching

fields include online communication, media innovation research, digital games and

journalism.

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